Endometritis subclinicas en ganado vacuno lechero

El objetivo ha sido ampliar el conocimiento en algunos aspectos relativos a las endometritis subclínicas (ES). Los resultados mostraron que las ES afectaron negativamente el rendimiento reproductivo y solo la citología uterina detectó la ES (casi un 15% de las vacas no fueron detectadas por ninguno otro método diagnóstico). Además, un sencillo método de evaluación de ES por contaje de %PMN existentes en 150 células, el número medio de PMN existente en 10 campos a 1000x y construyendo curvas ROC, permitió el diagnóstico de las ES en las citologías endometriales. Y, analizando tractos genitales postmortem se encontró una concordancia moderada entre la citología y la presencia de infiltrado en el endometrio, elevada entre ambos cuernos uterinos y escasa con la bacteriología. Concluimos que la citología parece el mejor método para el diagnostico de las ES por facilidad y rapidez en la obtención de los resultados

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning

[Abstract]: Background and Objectives: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some promising treatments have been proposed that effectively slow down its development, their effectiveness significantly diminishes in the advanced stages. This emphasizes the importance of large-scale screening programs for early detection. Nevertheless, implementing such programs for a disease like AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. For the characterization of the disease, clinicians have to identify and localize certain retinal lesions. All this motivates the development of automatic diagnostic methods. In this sense, several works have achieved highly positive results for AMD detection using convolutional neural networks (CNNs). However, none of them incorporates explainability mechanisms linking the diagnosis to its related lesions to help clinicians to better understand the decisions of the models. This is specially relevant, since the absence of such mechanisms limits the application of automatic methods in the clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions. Methods: In our proposal, a CNN with a custom architectural setting is trained end-to-end for the joint identification of AMD and its associated retinal lesions. With the proposed setting, the lesion identification is directly derived from independent lesion activation maps; then, the diagnosis is obtained from the identified lesions. The training is performed end-to-end using image-level labels. Thus, lesion-specific activation maps are learned in a weakly-supervised manner. The provided lesion information is of high clinical interest, as it allows clinicians to assess the developmental stage of the disease. Additionally, the proposed approach allows to explain the diagnosis obtained by the models directly from the identified lesions and their corresponding activation maps. The training data necessary for the approach can be obtained without much extra work on the part of clinicians, since the lesion information is habitually present in medical records. This is an important advantage over other methods, including fully-supervised lesion segmentation methods, which require pixel-level labels whose acquisition is arduous. Results: The experiments conducted in 4 different datasets demonstrate that the proposed approach is able to identify AMD and its associated lesions with satisfactory performance. Moreover, the evaluation of the lesion activation maps shows that the models trained using the proposed approach are able to identify the pathological areas within the image and, in most cases, to correctly determine to which lesion they correspond. Conclusions: The proposed approach provides meaningful information—lesion identification and lesion activation maps—that conveniently explains and complements the diagnosis, and is of particular interest to clinicians for the diagnostic process. Moreover, the data needed to train the networks using the proposed approach is commonly easy to obtain, what represents an important advantage in fields with particularly scarce data, such as medical imaging.Xunta de Galicia; ED481B-2022-025Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED481A 2021/140Xunta de Galicia; ED431G 2019/01This work was funded by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project; Ministerio de Ciencia e Innovación, Government of Spain, through RTI2018-095894-B-I00 and PID2019-108435RB-I00 research projects; Axencia Galega de Innovación (GAIN), Xunta de Galicia, ref. IN845D 2020/38; Conselleria de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva, ref. ED431C 2020/24, the predoctoral grant ref. ED481A 2021/140, and the postdoctoral grant ref. ED481B-2022-025; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, is funded by Conselleria de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaria Xeral de Universidades (20%)

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG.[Abstract]: Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.Xunta de Galicia; ED481B-2021-059Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/01This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with references PID2019-108435RB-I00; TED2021-131201B-I00 and PDC2022-133132-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the postdoctoral, grant ref. ED481B-2021-059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80 % through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20 % by “Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruña/CISUG

Subclinical Endometritis in Dairy Cattle

Subclinical endometritis is recognized as a cause of poor reproductive performance in dairy cows. Inflammation of the endometrium persisting after postpartum uterine involution has been related with prolonged calving-conception intervals and low fertility in dairy cows. The subclinical nature of this condition makes it necessary in the use of endometrial cytology or biopsy for diagnosing it. There are some controversies among authors in relation to the postpartum period from which a physiological endometrial inflammation should be considered a pathological subclinical endometritis. Therefore, depending on the sampling period after calving, different studies establish a different degree of polymorphonuclear leukocyte infiltration as cutoff point to diagnose subclinical endometritis. Controversies also exist regarding the pathogenesis of the disease and its consequences on the fertility of dairy cattle. The aim of this chapter was to review the current knowledge on this uterine pathology

Influence of subclinical endometritis on the reproductive performance of dairy cows

The aim of this study was to evaluate the influence of subclinical endometritis (SE) on the reproductive performance of dairy cows. Ninety-four dairy cows of parity 1 to 8, distributed in 25 herds, were examined once between 30 and 45 days in milk using transrectal palpation, vaginoscopy and ultrasonography. A cytological sample of the endometrium was taken only from cows with an apparent healthy uterus (n=65). Serum glucose, total cholesterol, triglycerides, non-esterified fatty acids, β-hydroxybutyrate, total proteins, albumin, urea and hepatic enzymes were analyzed. Reproductive indexes were recorded during the next 11 months. Endometrial cytology was considered indicative of SE if percentage of polymorphonuclear neutrophils was superior to 5% of all cells present in the smear, except erythrocytes. Results indicated that 14.9% of the cows sampled for uterine cytology had SE, and that healthy cows become pregnant significantly before than those with SE (hazard ratio=2.35; 95% confidece interval: 1.05-5.3). From all the metabolic and productive variables analyzed, only triglycerides affected negatively to reproduction; serum albumin concentration, body condition score and milk production had positive effects on the reproductive performance. In conclusion, our results indicate that SE has a negative impact on reproductive performance and uterine cytology is necessary to diagnose it since almost 15% of the affected animals were not detected by other diagnosis methodsXunta de Galicia (Programa Sectorial de Medio Rural, Proyecto Ref. PGIDIT07MRU002E) and FEFRIGA, Santiago de Compostela, SpainS

Validation of a simple method for the interpretation of uterine cytology in cows

One of the main drawbacks of using endometrial cytology in cows is the time required for sample collection and interpretation. It is recommended to count a large number of polymorphonuclear neutrophils (PMN) and to calculate their overall percentage. However, since counting a large number of cells is a laborious method, it would be preferable to simplify the analysis by counting the number of PMN in few microscopic fields. Therefore, the aim of this study was to assess whether a simple test, based on calculating the average number of PMN in 10 fields at 1000×, could be a reliable technique for the diagnosis of endometritis. Two hundred and sixty endometrial samples were taken from Holstein cows at different postpartum stages using an adapted cytobrush. Smears obtained were air-dried for fixing and stained with a Romanowsky-type procedure. To evaluate the counting method, the percentage of PMN in 150 cells was calculated as well as the average number of PMN in 10 fields at 1000×. Receiver operating characteristic (ROC) curves was constructed to evaluate both methods, the percentage of PMN (used as reference) and the average number of PMN. It was observed that the area under the curve is (regardless of cut-off used) higher than 0.99 and the correspondence between both methods were 1.58 PMN/field for the cut-off value of 15% and 2.40 PMN/field for the cut-off value of 20%. These results show that this simple method could be used to determine the percentage of PMN in endometrial cytological samples and to diagnose endometritis in cowsSupported by the Xunta de Galicia (Galician Plan for Research and Technological Development; Grant No. PGIDIT07MRU002E) and the Friesian Federation of Galician, A Coruna, SpainS

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by this disease by assessing the retinal layers in different regions of the eye, using different optical coherence tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of the retina. These images are used to measure the thickness of the retinal layers in different regions. Methods: We present two approaches for the multi-region segmentation of the retinal layers in OCT images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a single module to segment all of the scan patterns, considering them as a single domain. The second approach uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable module to analyse each image. Results: The proposed approaches produced satisfactory results with the first approach achieving a dice coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results for the better represented circle and cube scan patterns. Conclusions: To the extent of our knowledge, this is the first proposal in the literature for the multi-view segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learningbased systems for aiding in the diagnosis of this relevant pathology

Treatment variability and its relationships to outcomes among patients with Wernicke's encephalopathy: A multicenter retrospective study

Background: Despite guidelines and recommendations, Wernicke's encephalopathy (WE) treatment lacks evidence, leading to clinical practice variability.Aims: Given the overall lack of information on thiamine use for WE treatment, we analyzed data from a large, well-characterized multicenter sample of patients with WE, examining thiamine dosages; factors associated with the use of different doses, frequencies, and routes; and the influence of differences in thiamine treatment on the outcome.Methods: This retrospective study was conducted with data from 443 patients from 21 centers obtained from a nationwide registry of the Spanish Society of Internal Medicine (from 2000 to 2012). Discharge codes and Caine criteria were applied for WE diagnosis, and treatment-related (thiamine dosage, frequency, and route of administration) demographic, clinical, and outcome variables were analyzed.Results: We found marked variability in WE treatment and a low rate of high-dose intravenous thiamine administration. Seventy-eight patients out of 373 (20.9%) received > 300 mg/day of thiamine as initial dose. Patients fulfilling the Caine criteria or presenting with the classic WE triad more frequently received parenteral treatment. Delayed diagnosis (after 24 h hospitalization), the fulfillment of more than two Caine criteria at diagnosis, mental status alterations, and folic acid deficiency were associated significantly with the lack of complete recovery. Malnutrition, reduced consciousness, folic acid deficiency, and the lack of timely thiamine treatment were risk factors for mortality.Conclusions: Our results clearly show extreme variability in thiamine dosages and routes used in the management of WE. Measures should be implemented to ensure adherence to current guidelines and to correct potential nutritional deficits in patients with alcohol use disorders or other risk factors for WE

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues